SoroushVahidi/scidocs huggingface dataset:
Web-based expert systems: benefits and challenges
(2005)

Convergence of technologies in the Internet and the field of expert systems has offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-Advisor – for e-business strategy development, Fish-Expert – For fish disease diagnosis, and IMIS – to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.
Identification, characterization, and grounding of gradable terms in clinical text
(2016)

Gradable adjectives are inherently vague and are used by clinicians to document medical interpretations (e.g., severe reaction, mild symptoms). We present a comprehensive study of gradable adjectives used in the clinical domain. We automatically identify gradable adjectives and demonstrate that they have a substantial presence in clinical text. Further, we show that there is a specific pattern associated with their usage, where certain medical concepts are more likely to be described using these adjectives than others. Interpretation of statements using such adjectives is a barrier in medical decision making. Therefore, we use a simple probabilistic model to ground their meaning based on their usage in context.
A compact printed wide-slot UWB antenna with band-notched characteristics
(2016)

In this paper, we present an offset microstrip-fed ultrawideband antenna with band notched characteristics. The antenna structure consists of rectangular radiating patch and ground plane with rectangular shaped slot, which increases impedance bandwidth upto 123.52%(2.6–11GHz). A new modified U slot is etched in the radiating patch to create band-notched properties in the WiMAX (3.3–3.7GHz) and C-band satellite communication (3.7–4.15GHz). Furthermore, parametric studies have been conducted using EM simulation software CADFEKO suite(7.0). A prototype of antenna is fabricated on 1.6mm thick FR-4 substrate with dielectric constant of 4.4 and loss tangent of 0.02. The proposed antenna exhibits directional and omnidirectional radiation patterns along E and H-plane with stable efficiency over the frequency band from 2.6GHz to 11GHz with VSWR less than 2, except 3.3–4.15GHz notched frequency band. The proposed antenna shows good time domain analysis.
Causes and consequences of microRNA dysregulation in cancer
(2009)

Over the past several years it has become clear that alterations in the expression of microRNA (miRNA) genes contribute to the pathogenesis of most — if not all — human malignancies. These alterations can be caused by various mechanisms, including deletions, amplifications or mutations involving miRNA loci, epigenetic silencing or the dysregulation of transcription factors that target specific miRNAs. Because malignant cells show dependence on the dysregulated expression of miRNA genes, which in turn control or are controlled by the dysregulation of multiple protein-coding oncogenes or tumour suppressor genes, these small RNAs provide important opportunities for the development of future miRNA-based therapies.
A review on memristor applications
(2015)

This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led by Stanley Williams. In particular, after a brief analysis of memristor theory with a description of the first memristor, manufactured at HP Laboratories, we present its main applications in the circuit design and computer technology, together with future developments.
A Customer Loyalty Model for E-Service Context
(2003)

While the importance of customer loyalty has been recognized in the marketing literature for at least three decades, the conceptualization and empirical validation of a customer loyalty model for e-service context has not been addressed. This paper describes a theoretical model for investigating the three main antecedent influences on loyalty (attitudinal commitment and behavioral loyalty) for e-service context: trust, customer satisfaction, and perceived value. Based on the theoretical model, a comprehensive set of hypotheses were formulated and a methodology for testing them was outlined. These hypotheses were tested empirically to demonstrate the applicability of the theoretical model. The results indicate that trust, customer satisfaction, perceived value, and commitment are separate constructs that combine to determine the loyalty, with commitment exerting a stronger influence than trust, customer satisfaction, and perceived value. Customer satisfaction and perceived value were also indirectly related to loyalty through commitment. Finally, the authors discuss the managerial and theoretical implications of these results.
A multi-scale target detection method for optical remote sensing images
(2018)

Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which has both rapid speed and high detection accuracy. However, when the model is applied to the target detection of remote sensing imagery, faced with multi-scale targets, its performance is degraded. We analyze the influences of pooling operation and target size on region proposal, then a modified solution for region proposal is introduced to improve recall rate of multi-scale targets. To speed up the convergence of the region proposal networks, an improved generation strategy of foreground samples is proposed, which could suppresses the generation of non-effective foreground samples. Extensive evaluations on the remote sensing image dataset show that the proposed model can obviously improve detection accuracy for multi-scale targets, moreover the training of the model is rapid and high-efficient.
A Linearly Relaxed Approximate Linear Program for Markov Decision Processes
(2018)

Approximate linear programming (ALP) and its variants have been widely applied to Markov decision processes (MDPs) with a large number of states. A serious limitation of ALP is that it has an intractable number of constraints, as a result of which constraint approximations are of interest. In this paper, we define a linearly relaxed approximation linear program (LRALP) that has a tractable number of constraints, obtained as positive linear combinations of the original constraints of the ALP. The main contribution is a novel performance bound for LRALP.
Data science is science's second chance to get causal inference right: A classification of data science tasks
(2018)

1. Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 2. Harvard-MIT Division of Health Sciences and Technology, Boston, MA 3. Mongan Institute, Massachusetts General Hospital, Boston, MA 4. Department of Health Care Policy, Harvard Medical School, Boston, MA 5. Department of Neurology, Harvard Medical School, Partners MS Center, Brigham and Women’s Hospital, Boston, MA 6. Biostatistics Center, Massachusetts General Hospital, Boston, MA
Attention Alignment Multimodal LSTM for Fine-Gained Common Space Learning
(2018)

We address the problem common space learning approach that maps all related multimodal information into a common space for multimodal data. To establish a fine-grained common space, the aligned relevant local information of different modalities is used to learn a common subspace where the projected fragmented information is further integrated according to intra-modal semantic relationships. Specifically, we propose a novel multimodal LSTM with an attention alignment mechanism, namely attention alignment multimodal LSTM (AAM-LSTM), which mainly includes attentional alignment recurrent network (AA-R) and hierarchical multimodal LSTM (HM-LSTM). Different from the traditional methods which operate on the full modal data directly, the proposed model exploits the inter-modal and intra-modal semantic relationships of local information, to jointly establish a uniform representation of multi-modal data. Specifically, AA-R automatically captures semantic-aligned local information to learn common subspace without the need of supervised labels, then HM-LSTM leverages the potential relationships of these local information to learn a fine-grained common space. The experimental results on Filker30K, Filker8K, and Filker30K entities verify the performance and effectiveness of our model, which compares favorably with the state-of-the-art methods. In particular, the experiment of phrase localization on AA-R with Filker30K entities shows the expected accurate attention alignment. Moreover, from the experiment results of image-sentence retrieval tasks, it can be concluded that the proposed AAM-LSTM outperforms benchmark algorithms by a large margin.
Chip-level and board-level CDM ESD tests on IC products
(2009)

The electrostatic discharge (ESD) transient currents and failure analysis (FA) between chip-level and board-level charged-device-model (CDM) ESD tests are investigated in this work. The discharging current waveforms of three different printed circuit boards (PCBs) are characterized first. Then, the chip-level and board-level CDM ESD tests are performed to an ESD-protected dummy NMOS and a high-speed receiver front-end circuit, respectively. Scanning electron microscope (SEM) failure pictures show that the board-level CDM ESD test causes much severer failure than that caused by the chip-level CDM ESD test.
Classifying Objectionable Websites Based on Image Content
(1998)

This paper describes IBCOW (Image-based Classi cation of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPETM (Wavelet Image Pornography Elimination) and statistics to provide robust classi cation of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as "objectionable" and "benign" in a training set can be performed efciently and e ectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% speci city in classifying a Web site based solely on images. Both the sensitivity and the speci city in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classi cation process.
Knowledge-Based Distant Regularization in Learning Probabilistic Models
(2018)

Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.
Representation Learning for Grounded Spatial Reasoning
(2018)

The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.
Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art
(2008)

In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.
Brain–machine interfaces: past, present and future
(2006)

Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.
(2005)

Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.
Power assist method for HAL-3 using EMG-based feedback controller
(2003)

We have developed the exoskeletal robotics suite HAL (Hybrid Assisitve Leg) which is integrated with human and assists suitable power for ‘lower limb of people with goit disorder. This study proposes the method of assist motion and ossist torque to realize a power assist corresponding to the operator’s intention. In the method of a,ssist motion, we adopted Phase Sequence control which generates a series of assist m,otions hi/ transiti.n,q .some simple basic motions called Phase. w e used the feedback controller to adjust the assist torque to m.ointain myoelectricity signals which were generated while performing the power assist uiolking. The experiment xsults showed the effective power assist according to operator’s intention b y using these control methods.
Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait
(2004)

An active ankle-foot orthoses (AAFO) is presented where the impedance of the orthotic joint is modulated throughout the walking cycle to treat drop-foot gait. During controlled plantar flexion, a biomimetic torsional spring control is applied where orthotic joint stiffness is actively adjusted to minimize forefoot collisions with the ground. Throughout late stance, joint impedance is minimized so as not to impede powered plantar flexion movements, and during the swing phase, a torsional spring-damper control lifts the foot to provide toe clearance. To assess the clinical effects of variable-impedance control, kinetic and kinematic gait data were collected on two drop-foot participants wearing the AAFO. For each participant, zero, constant, and variable impedance control strategies were evaluated and the results were compared to the mechanics of three age, weight, and height matched normals. We find that actively adjusting joint impedance reduces the occurrence of slap foot allows greater powered plantar flexion and provides for less kinematic difference during swing when compared to normals. These results indicate that a variable-impedance orthosis may have certain clinical benefits for the treatment of drop-foot gait compared to conventional ankle-foot orthoses having zero or constant stiffness joint behaviors.
Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting
(2009)

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. Keywords—Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting
Ultrasound-Assisted Evaluation ofthe Airway in Clinical AnesthesiaPractice: Past, Present and Future
(2015)

Introduction: The incidence of difficulties encountered in perioperative airway management has been reported to range from 1% to 4%. In patients with head and neck cancers, the incidence can be dramatically higher. Because of high quality of imaging, non-invasiveness and relatively low cost, ultrasonography has been utilized as a valuable adjunct to the clinical assessment of the airway. A review of the literature was conducted with the objective of summarizing the available evidence concerning the use of ultrasound (US) for assessment of the airway, with special emphasis on head and neck cancers. Methods and Materials: A systematic search of the literature in the MEDLINE database was performed. A total of 42 manuscripts from 329 searched articles were included in this review. Results: Ultrasonography was found to give high-resolution images of the anatomic structures of the upper airway comparable to computed tomography and magnetic resonance imaging. Several ultrasonographic parameters (soft tissue thickness at level of hyoid bone, epiglottis and vocal cords, visibility of hyoid bone in sublingual ultrasound, hyomental distance in head-extended position and hyomental distance ratio) were found to be independent predictors of difficult laryngoscopy in obese and non-obese patients. In conjunction with elastosonography, it also provided valuable information regarding tumors, infiltration, and edema as well as fibrosis of the head and neck. Conclusion: Ultrasound-assisted evaluation of the difficult airway offers many important advantages. The ready availability of US machines in anesthesiology departments, familiarity of anesthesia providers with USguided procedures and the portability of US machines allow real-time, point-of-care assessment. It will undoubtedly become more popular and will greatly contribute to improve perioperative patient safety.
Time series forecasting using Artificial Neural Networks vs. evolving models
(2014)

Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.
Practice Makes Perfect ? When Does Massed Learning Improve Product Usage Proficiency ?
(2010)

Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.